References of "Keller, Ulrich 50002080"
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See detailTackling educational inequalities using school effectiveness measures
Levy, Jessica UL; Mussack, Dominic UL; Brunner, Martin et al

Scientific Conference (2020, November 11)

Detailed reference viewed: 73 (11 UL)
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See detailIs there a bilingual advantage in mathematics?
Martini, Sophie Frédérique UL; Keller, Ulrich UL; Ugen, Sonja UL

Scientific Conference (2020, November)

Detailed reference viewed: 21 (2 UL)
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See detailSelf-concept, interest, and achievement within and across math and verbal domains in first- and third-graders
van der Westhuizen, Lindie UL; Arens, A. Katrin; Keller, Ulrich UL et al

Scientific Conference (2020, April)

The generalized internal/external frame-of-reference (G)I/E model explains the formation of domain-specific motivational-affective constructs through social and dimensional comparisons. We examined the ... [more ▼]

The generalized internal/external frame-of-reference (G)I/E model explains the formation of domain-specific motivational-affective constructs through social and dimensional comparisons. We examined the associations between verbal and math achievement and corresponding domain-specific academic self-concepts (ASCs) and interests for first-graders and third-graders (N=21,192). Positive achievement-self-concept and achievement-interest relations were found within matching-domains in both grades, while negative cross-domains achievement-self-concept and achievement-interest relations were only found for third-graders. These findings suggest that while the formation of domain-specific ASCs and interests seem to rely on social and dimensional comparisons for third-graders, only social comparisons seem to be in operation for first-graders. Gender and cohort invariance was established in both grade levels. Findings are discussed within the framework of ASC differentiation and dimensional comparison theory. [less ▲]

Detailed reference viewed: 37 (1 UL)
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See detailLangzeiteffekte von Klassenwiederholungen in der Sekundarstufe
Klapproth, Florian; Keller, Ulrich UL; Fischbach, Antoine UL

Scientific Conference (2020, March)

Detailed reference viewed: 32 (2 UL)
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See detailContrasting Classical and Machine Learning Approaches in the Estimation of Value-Added Scores in Large-Scale Educational Data
Levy, Jessica UL; Mussack, Dominic UL; Brunner, Martin et al

in Frontiers in Psychology (2020), 11

There is no consensus on which statistical model estimates school value-added (VA) most accurately. To date, the two most common statistical models used for the calculation of VA scores are two classical ... [more ▼]

There is no consensus on which statistical model estimates school value-added (VA) most accurately. To date, the two most common statistical models used for the calculation of VA scores are two classical methods: linear regression and multilevel models. These models have the advantage of being relatively transparent and thus understandable for most researchers and practitioners. However, these statistical models are bound to certain assumptions (e.g., linearity) that might limit their prediction accuracy. Machine learning methods, which have yielded spectacular results in numerous fields, may be a valuable alternative to these classical models. Although big data is not new in general, it is relatively new in the realm of social sciences and education. New types of data require new data analytical approaches. Such techniques have already evolved in fields with a long tradition in crunching big data (e.g., gene technology). The objective of the present paper is to competently apply these “imported” techniques to education data, more precisely VA scores, and assess when and how they can extend or replace the classical psychometrics toolbox. The different models include linear and non-linear methods and extend classical models with the most commonly used machine learning methods (i.e., random forest, neural networks, support vector machines, and boosting). We used representative data of 3,026 students in 153 schools who took part in the standardized achievement tests of the Luxembourg School Monitoring Program in grades 1 and 3. Multilevel models outperformed classical linear and polynomial regressions, as well as different machine learning models. However, it could be observed that across all schools, school VA scores from different model types correlated highly. Yet, the percentage of disagreements as compared to multilevel models was not trivial and real-life implications for individual schools may still be dramatic depending on the model type used. Implications of these results and possible ethical concerns regarding the use of machine learning methods for decision-making in education are discussed. [less ▲]

Detailed reference viewed: 82 (9 UL)
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See detailNeed for Cognition across school tracks: The importance of learning environments
Colling, Joanne UL; Wollschläger, Rachel UL; Keller, Ulrich UL et al

Scientific Conference (2019, November 06)

Detailed reference viewed: 95 (8 UL)
See detailDimensional and Social Comparison Effects on Domain-Specific Academic Self-Concepts and Interests with First- and Third-Grade Students
van der Westhuizen, Lindie UL; Arens, Katrin; Keller, Ulrich UL et al

Scientific Conference (2019, November 06)

Academic self-concepts (ASCs) are self-perceptions of one’s own academic abilities. The internal/external frame of reference (I/E) model (Marsh, 1986) explains the formation of domain-specific ASCs ... [more ▼]

Academic self-concepts (ASCs) are self-perceptions of one’s own academic abilities. The internal/external frame of reference (I/E) model (Marsh, 1986) explains the formation of domain-specific ASCs through a combination of social (i.e. comparing one’s achievement in one domain with the achievement of others in the same domain) and dimensional (i.e. comparing one’s achievement in one domain with one’s achievement in another domain) comparisons. This results into positive achievement-self-concept relations within the math and verbal domains, but into negative achievement-self-concept relations across these domains. The generalized internal/external frame of reference (GI/E) model (Möller, Müller-Kalthoff, Helm, Nagy, & Marsh, 2015) extends the I/E model to the formation of other domain-specific academic self-beliefs such as interest. Research on the validity of the (G)I/E model for elementary school children is limited, especially for first-graders. This study examined the associations between verbal and math achievement and corresponding domain-specific self-concepts and interests for first-graders and third-graders. Two fully representative Luxembourgish first-grader cohorts and two fully representative third-graders cohorts (N=21,192) were used. The analyses were based on structural equation modeling. The findings fully supported the (G)I/E model for third-graders: Achievement was positively related to self-concept and interest within matching domains. Negative relations were found between achievement and self-concept and between achievement and interest across domains. For first-graders, achievement was positively related to self-concept and interest within matching domains. However, the majority of cross-domain relations were non-significant, except for the negative path between math achievement and verbal interest. Hence, while the formation of domain-specific ASCs and interests seem to rely on social and dimensional comparisons for third-graders, only social comparisons seem to be in operation for first-graders. Gender and cohort invariance was established for both grade levels. The findings are discussed within the framework of ASC differentiation and dimensional comparison theory applied to elementary school students. [less ▲]

Detailed reference viewed: 117 (6 UL)
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See detailSimilarities and differences of value-added scores from models with different covariates: A cluster analysis
Levy, Jessica UL; Brunner, Martin; Keller, Ulrich UL et al

Scientific Conference (2019, November 06)

Detailed reference viewed: 73 (5 UL)
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See detailValue-added models: To what extent do estimates of school effectiveness depend on the selection of covariates?
Levy, Jessica UL; Brunner, Martin; Keller, Ulrich UL et al

Scientific Conference (2019, September)

Detailed reference viewed: 79 (5 UL)
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See detailMethodological Issues in Value-Added Modeling: An International Review from 26 Countries
Levy, Jessica UL; Brunner, Martin; Keller, Ulrich UL et al

in Educational Assessment, Evaluation and Accountability (2019), 31(3), 257-287

Value-added (VA) modeling can be used to quantify teacher and school effectiveness by estimating the effect of pedagogical actions on students’ achievement. It is gaining increasing importance in ... [more ▼]

Value-added (VA) modeling can be used to quantify teacher and school effectiveness by estimating the effect of pedagogical actions on students’ achievement. It is gaining increasing importance in educational evaluation, teacher accountability, and high-stakes decisions. We analyzed 370 empirical studies on VA modeling, focusing on modeling and methodological issues to identify key factors for improvement. The studies stemmed from 26 countries (68% from the USA). Most studies applied linear regression or multilevel models. Most studies (i.e., 85%) included prior achievement as a covariate, but only 2% included noncognitive predictors of achievement (e.g., personality or affective student variables). Fifty-five percent of the studies did not apply statistical adjustments (e.g., shrinkage) to increase precision in effectiveness estimates, and 88% included no model diagnostics. We conclude that research on VA modeling can be significantly enhanced regarding the inclusion of covariates, model adjustment and diagnostics, and the clarity and transparency of reporting. [less ▲]

Detailed reference viewed: 215 (32 UL)
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See detailValue-added modeling in primary school: What covariates to include?
Levy, Jessica UL; Brunner, Martin; Keller, Ulrich UL et al

Scientific Conference (2019, August)

Detailed reference viewed: 118 (8 UL)
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See detailThe use of value-added models for the identification of schools that perform “against the odds”
Levy, Jessica UL; Brunner, Martin; Keller, Ulrich UL et al

Poster (2019, July)

Value-added (VA) modeling aims to quantify the effect of pedagogical actions on students’ achievement, independent of students’ backgrounds. VA modeling is primarily used for accountability and high ... [more ▼]

Value-added (VA) modeling aims to quantify the effect of pedagogical actions on students’ achievement, independent of students’ backgrounds. VA modeling is primarily used for accountability and high-stakes decisions. To date, there seems to be no consensus concerning the calculation of VA models. Our study aims to systematically analyze and compare different school VA models by using longitudinal large-scale data emerging from the Luxembourg School Monitoring Programme. Regarding the model covariates, first findings indicate the importance of language (i.e., language(s) spoken at home and prior language achievement) in VA models with either language or math achievement as a dependent variable, with the highest amount of explained variance in VA models for language. Concerning the congruence of different VA approaches, we found high correlations between school VA scores from the different models, but also high ranges between VA scores for single schools. We conclude that VA models should be used with caution and with awareness of the differences that may arise from methodological choices. Finally, we discuss the idea that VA models could be used for the identification of schools that perform “against the odds”, especially for those schools that have positive VA scores over several years. [less ▲]

Detailed reference viewed: 76 (5 UL)
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See detailExploration of Different School Value-Added Models in a Highly Heterogeneous Educational Context
Levy, Jessica UL; Brunner, Martin; Keller, Ulrich UL et al

Scientific Conference (2019, April)

Detailed reference viewed: 101 (15 UL)
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See detailA need for cognition scale for children and adolescents: Structural analysis and measurement invariance
Keller, Ulrich UL; Strobel, Anja; Wollschläger, Rachel UL et al

in European Journal of Psychological Assessment (2019), 35

Need for Cognition (NFC) signifies “the tendency for an individual to engage in and enjoy thinking” (Cacioppo & Petty, 1982, p. 116). Up to now, no scale of sufficient psychometric quality existed to ... [more ▼]

Need for Cognition (NFC) signifies “the tendency for an individual to engage in and enjoy thinking” (Cacioppo & Petty, 1982, p. 116). Up to now, no scale of sufficient psychometric quality existed to assess NFC in children. Using data from three independent, diverse cross-sectional samples from Germany, Luxembourg, and Finland, we examined the psychometric properties of a new NFC scale intended to fill in this gap. In all samples, across grades levels ranging from 1 to 9, confirmatory factor analysis confirmed the hypothesized nested factor structure based on Mussel’s (2013) Intellect model, with one general factor Think influencing all items and two specific factors Seek and Conquer each influencing a subset of items. At least partial scalar measurement invariance with regard to grade level and sex could be demonstrated. The scale exhibited good psychometric properties and showed convergent and discriminant validity with an established NFC scale and other non-cognitive traits such as academic self-concept and interests. It incrementally predicted mostly statistically significant but relatively small portions of academic achievement variance over and above academic self-concept and interest. Implications for research on the development of NFC and its role as an investment trait in intellectual development are discussed. [less ▲]

Detailed reference viewed: 196 (43 UL)
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See detailSchülerkompetenzen im Längsschnitt - Die Entwicklung von Deutsch-Leseverstehen und Mathematik in Luxemburg zwischen der 3. und 9. Klasse
Sonnleitner, Philipp UL; Krämer, Charlotte UL; Gamo, Sylvie UL et al

Report (2018)

it der Erhebung der ÉpStan im Herbst 2016 liegt erstmalig ein Datensatz vor, der einen Einblick in die Entwicklung schulischer Kompetenzen zwischen der 3. Schulstufe (Zyklus 3.1) und der 9. Schulstufe (5e ... [more ▼]

it der Erhebung der ÉpStan im Herbst 2016 liegt erstmalig ein Datensatz vor, der einen Einblick in die Entwicklung schulischer Kompetenzen zwischen der 3. Schulstufe (Zyklus 3.1) und der 9. Schulstufe (5e bzw. 9e) erlaubt. Das vorliegende Kapitel gibt nun einen ersten Einblick in die längsschnittliche Kompetenzentwicklung in den Bereichen Deutsch-Leseverstehen und Mathematik. Hierfür werden die Testergebnisse der untersuchten Schülerkohorte aus den ÉpStan 2010 in der 3. Schulstufe (Zyklus 3.1) den Leistungen in der 9. Schulstufe (5e bzw. 9e) im Jahre 2016 gegenübergestellt. [less ▲]

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See detailSchulische Kompetenzen von Erstklässlern und ihre Entwicklung nach zwei Jahren.
Hoffmann, Danielle UL; Hornung, Caroline UL; Gamo, Sylvie UL et al

Report (2018)

Dieses Kapitel stellt die Befunde aus drei Datenerhebungen (2014, 2015, 2016) der ÉpStan im Zyklus 2.1 vor und zeigt welche schulischen Kompetenzen Erstklässler am Anfang ihrer Schullaufbahn aufweisen und ... [more ▼]

Dieses Kapitel stellt die Befunde aus drei Datenerhebungen (2014, 2015, 2016) der ÉpStan im Zyklus 2.1 vor und zeigt welche schulischen Kompetenzen Erstklässler am Anfang ihrer Schullaufbahn aufweisen und wie sich diese über zwei Jahre hinweg entwickeln. Allgemein betrachtet, sind die für den Zyklus 1 festgehaltenen Bildungsstandards in den drei überprüften Kernkompetenzen („Luxemburgisch-Hörverstehen“, „Vorläuferfertigkeiten der Schriftsprache“ und „Mathematik“) erfüllt. In allen drei Kompetenzen erreicht die Mehrheit der Schülerinnen und Schüler zu Beginn des Zyklus 2.1 das Niveau Avancé. Zwei Jahre später, im Zyklus 3.1, fällt die Verteilung der Schülerinnen und Schüler auf die verschiedenen Kompetenzränge negativer aus als im Zyklus 2.1. Hier haben vergleichsweise mehr Kinder das Niveau Socle in allen drei Kernkompetenzen noch nicht erreicht. Unsere Befunde zeigen außerdem, dass verschiedene außerschulische Faktoren (wie z. B. sozioökonomische Situation, Sprachhintergrund) bereits sehr früh im Verlauf der Schullaufbahn einen äußerst starken Einfluss auf die Testergebnisse haben und dass sich dieser Einfluss über die Jahre hinweg verstärkt. [less ▲]

Detailed reference viewed: 127 (8 UL)